In pattern mining, sequential rules provide a formal framework to capture the temporal relationships and inferential dependencies between items. However, the discovery process is computationally intensive. To obtain mining results efficiently and flexibly, many methods have been proposed that rely on specific evaluation metrics (i.e., ensuring results meet minimum threshold requirements). A key issue with these methods, however, is that they generate many sequential rules that are irrelevant to users. Such rules not only incur additional computational overhead but also complicate downstream analysis. In this paper, we investigate how to efficiently discover user-centric sequential rules. The original database is first processed to determine whether a target query rule is present. To prune unpromising items and avoid unnecessary expansions, we design tight and generalizable upper bounds. We introduce a novel method for efficiently generating target sequential rules using the proposed techniques and pruning strategies. In addition, we propose the corresponding mining algorithms for two common evaluation metrics: frequency and utility. We also design two rule similarity metrics to help discover the most relevant sequential rules. Extensive experiments demonstrate that our algorithms outperform state-of-the-art approaches in terms of runtime and memory usage, while discovering a concise set of sequential rules under flexible similarity settings. Targeted sequential rule search can handle sequence data with personalized features and achieve pattern discovery. The proposed solution addresses several challenges and can be applied to two common mining tasks.